A segmentation approach for stochastic geological modeling using hidden Markov random fields

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Title:Main Title: A segmentation approach for stochastic geological modeling using hidden Markov random fields
Description:Abstract: Stochastic modeling methods and uncertainty quantification are important tools for gaining insight into the geological variability of subsurface structures. Previous attempts at geologic inversion and interpretation can be broadly categorized into geostatistics and process-based modeling. The choice of a suitable modeling technique directly depends on the modeling applications and the available input data. Modern geophysical techniques provide us with regional data sets in two- or three-dimensional spaces with high resolution either directly from sensors or indirectly from geophysical inversion. Existing methods suffer certain drawbacks in producing accurate and precise (with quantified uncertainty) geological models using these data sets. In this work, a stochastic modeling framework is proposed to extract the subsurface heterogeneity from multiple and complementary types of data. Subsurface heterogeneity is considered as the “hidden link” between multiple spatial data sets. Hidden Markov random field models are employed to perform three-dimensional segmentation, which is the representation of the “hidden link”. Finite Gaussian mixture models are adopted to characterize the statistical parameters of multiple data sets. The uncertainties are simulated via a Gibbs sampling process within a Bayesian inference framework. The proposed modeling method is validated and is demonstrated using numerical examples. It is shown that the proposed stochastic modeling framework is a promising tool for three-dimensional segmentation in the field of geological modeling and geophysics.
Identifier:10.1007/s11004-016-9663-9 (DOI)
Citation Advice:Wang, H., Wellmann, J. F., Li, Z., Wang, X., & Liang, R. Y. (2017). A segmentation approach for stochastic geological modeling using hidden Markov random fields. Mathematical Geosciences, 49(2), 145-177.
Responsible Party
Creators:Hui Wang (Author), Florian Wellmann (Author), Zhao Li (Author), Robert Y. Liang (Author), Xiangrong Wang (Author)
Publisher:Springer Berlin Heidelberg
Publication Year:2018
Topic
TR32 Topic:Soil
Related Subproject:B9
Subject:Keyword: Modelling
Geogr. Information Topic:Geoscientific Information
File Details
Filename:2016_Wang_etal_A_Segmentation_Approach_for_Stochastic_Geological_model.pdf
Data Type:Text - Article
File Size:3.6 MB
Date:Created: 23.05.2018
Mime Type:application/pdf
Language:English
Status:Completed
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Download Permission:Free
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
Licence:[TR32DB] Data policy agreement
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Publication Status:In Print
Review Status:Peer reviewed
Publication Type:Article
Article Type:Journal
Source:Mathematical Geosciences
Issue:2
Volume:49
Number of Pages:33 (145 - 177)
Metadata Details
Metadata Creator:Hui Wang
Metadata Created:23.05.2018
Metadata Last Updated:23.05.2018
Subproject:B9
Funding Phase:3
Metadata Language:English
Metadata Version:V50
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